Instructions to use abaddon182/9db6cbba-2a7f-4aa3-98b7-fa898ca8f99a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abaddon182/9db6cbba-2a7f-4aa3-98b7-fa898ca8f99a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("echarlaix/tiny-random-mistral") model = PeftModel.from_pretrained(base_model, "abaddon182/9db6cbba-2a7f-4aa3-98b7-fa898ca8f99a") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from abaddon182/9db6cbba-2a7f-4aa3-98b7-fa898ca8f99a: direct link, hf CLI and curl.
- Browser
- Download file 120 kB
-
https://huggingface.co/abaddon182/9db6cbba-2a7f-4aa3-98b7-fa898ca8f99a/resolve/main/adapter_model.bin
- Command line
-
hf download hf://abaddon182/9db6cbba-2a7f-4aa3-98b7-fa898ca8f99a/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/abaddon182/9db6cbba-2a7f-4aa3-98b7-fa898ca8f99a/resolve/main/adapter_model.bin
120 kB
- Xet hash:
- da613e7e3c29973bc3dee286af380f867109d84dac4472776d262184cf719825
- Size of remote file:
- 120 kB
- SHA256:
- 5d7b619fe41689f69ee701da1c75c79dd30fc8391167a2977636c0edaef2e8b6
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